Started with the broad strokes: core components, how a post travels from upload to someone's feed, where you'd need to scale.
Start by clarifying functional and non-functional requirements, then propose a high-level architecture that separates feed generation from delivery. Focus on the feed pipeline: how to efficiently generate personalized feeds for millions of users and deliver them with low latency, discussing trade-offs between precomputation and on-demand generation.
Pro tip: Emphasize the trade-off between precomputing feeds (fast reads, expensive writes) and generating on-the-fly (flexible, but higher latency), and propose a hybrid approach that balances both based on user activity and follower count.
Ask about scale (DAU, photos per user), latency requirements, feed freshness, and personalization needs. Confirm whether the feed is reverse-chronological or ranked.
Outline core components: photo upload service, feed generation service, feed storage/cache, and delivery API. Mention data stores like object storage for photos and a graph database for social connections.
Discuss push vs. pull models. For push, use a fan-out service to write to followers' feed caches; for pull, generate on read. Propose a hybrid: push for active users, pull for celebrities.
Explain how to serve feeds with low latency using in-memory caches (e.g., Redis) and CDNs for media. Discuss pagination, cursor-based fetching, and consistency trade-offs.
Address scaling challenges: hotkeys, celebrity problem, storage costs, and real-time updates. Discuss monitoring, failure handling, and potential optimizations like ranking algorithms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements (e.g., read vs write patterns, latency, consistency). Then propose a hybrid architecture that combines fan-out-on-write for most users with fan-out-on-read for celebrity accounts, and discuss trade-offs like latency, cost, and complexity.
Pro tip: Mention that the threshold for switching between fan-out strategies should be dynamic and based on follower count and activity, and that you'd monitor and adjust it. This shows you think about operational maturity, not just theoretical design.
Ask about scale (number of users, followers, posts per day), latency expectations, consistency needs, and read/write ratio. This ensures your design targets the right constraints.
Compare fan-out-on-write (push) vs fan-out-on-read (pull). Explain that push is great for low-latency reads but expensive for celebrities, while pull is cheaper for writes but adds read latency.
Suggest a hybrid: use push for normal users and pull for celebrities. For celebrities, store their posts separately and merge at read time. This balances latency and cost.
Discuss trade-offs: increased read complexity, potential latency spikes, and cache strategies. Mention optimizations like pre-computing feeds for active users, using CDNs, and caching celebrity posts.
Explain how to scale the solution (sharding, replication) and monitor performance (latency, error rates). Suggest dynamic thresholds for switching strategies based on follower count.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Follow-up that came straight from the celebrity question.
Start by clarifying the scale and read patterns of the feed delivery layer, then propose a multi-layered caching strategy with read replicas and denormalization. Discuss trade-offs between consistency, latency, and cost, and how to handle cache invalidation and hot keys.
Pro tip: Emphasize that scaling reads is not just about adding caches; it's about understanding the access patterns and designing for the common case while gracefully handling the tail. Mention that at OpenAI, feed delivery often involves personalized content, so precomputation and edge caching can be game-changers.
Ask about read volume, latency SLAs, data size, and consistency requirements to scope the problem.
Analyze the current architecture to find where reads are hitting the database directly and causing contention.
Introduce client-side, CDN, application-level, and database caching with appropriate TTLs and invalidation strategies.
Use read replicas to distribute load and consider sharding or partitioning for horizontal scaling.
Evaluate consistency vs. availability, cost implications, and set up monitoring for cache hit rates and replication lag.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.